145 citations · 234 across the 14 of their papers we have counts for
16 papers
A Differentiable Loss Function for Learning Heuristics in A*
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values. T…
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic…
Knowledge-Based Paranoia Search in Trick-Taking
Stefan Edelkamp
This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games fo…
ELO System for Skat and Other Games of Chance
Stefan Edelkamp
Assessing the skill level of players to predict the outcome and to rank the players in a longer series of games is of critical importance for tournament play. Besides weaknesses, l…
On the Power of Refined Skat Selection
Stefan Edelkamp
Skat is a fascinating combinatorial card game, show-casing many of the intrinsic challenges for modern AI systems such as cooperative and adversarial behaviors (among the players),…
QuickXsort - A Fast Sorting Scheme in Theory and Practice
Stefan Edelkamp, Armin Weiß, Sebastian Wild
QuickXsort is a highly efficient in-place sequential sorting scheme that mixes Hoare's Quicksort algorithm with X, where X can be chosen from a wider range of other known sorting a…